Geo-Referenced Occlusion Models for Mixed Reality Applications using the Microsoft HoloLens

Christoph Praschl, Oliver Krauss

2022

Abstract

Emergency responders or task forces can benefit from outdoor Mixed Reality (MR) trainings, as they allow more realistic and affordable simulations of real-world emergencies. Utilizing MR devices for outdoor situations requires knowledge of real-world objects in the training area, enabling the realistic immersion of both, the real, as well as the virtual world, based on visual occlusions. Due to spatial limitations of state-of-the-art MR devices recognizing distant real-world items, we present an approach for sharing geo-referenced 3D geometries across multiple devices utilizing the CityJSON format for occlusion purposes in the context of geospatial MR visualization. Our results show that the presented methodology allows accurate conversion of occlusion models to geo-referenced representations based on a quantitative evaluation with an average error according to the vertices’ position from 1.30E-06 to 2.79E-04 (sub-millimeter error) using a normalized sum of squared errors metric. In the future, we plan to also incorporate 3D reconstructions from smartphones and drones to increase the number of supported devices for creating geo-referenced occlusion models.

Paper Citation

in Harvard Style

Praschl C. and Krauss O. (2022). Geo-Referenced Occlusion Models for Mixed Reality Applications using the Microsoft HoloLens. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 3: IVAPP; ISBN 978-989-758-555-5, SciTePress, pages 113-122. DOI: 10.5220/0010775200003124

in Bibtex Style

@conference{ivapp22,
author={Christoph Praschl and Oliver Krauss},
title={Geo-Referenced Occlusion Models for Mixed Reality Applications using the Microsoft HoloLens},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 3: IVAPP},
year={2022},
pages={113-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010775200003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 3: IVAPP
TI - Geo-Referenced Occlusion Models for Mixed Reality Applications using the Microsoft HoloLens
SN - 978-989-758-555-5
AU - Praschl C.
AU - Krauss O.
PY - 2022
SP - 113
EP - 122
DO - 10.5220/0010775200003124
PB - SciTePress